Using a Novel Machine Learning Technique to Predict Radiation Oncology Outcomes

被引：0

作者：

Muhlestein, W. ^{[1
]}

Chambless, L. B. ^{[2
]}

Pajewski, N. M. ^{[3
]}

Braunstein, S. E. ^{[4
]}

Hepel, J. T. ^{[5
]}

Chung, C. ^{[6
]}

Contessa, J. N. ^{[7
]}

Chao, S. T. ^{[8
]}

Fiveash, J. B. ^{[9
]}

Attia, A. ^{[10
]}

Chan, M. D. ^{[11
]}

Ayala-Peacock, D. N. ^{[12
]}

机构：

[1] Vanderbilt Univ, Med Ctr, Nashville, TN USA

[2] Vanderbilt Univ, Med Ctr, Dept Neurosurg, Nashville, TN USA

[3] Wake Forest Baptist Med Ctr, Winston Salem, NC USA

[4] Univ Calif San Francisco, San Francisco, CA 94143 USA

[5] Brown Univ, Rhode Isl Hosp, Radiat Oncol, Alpert Med Sch, Providence, RI 02903 USA

[6] Princess Margaret Hosp, Toronto, ON, Canada

[7] Yale Univ, Sch Med, Dept Therapeut Radiol, New Haven, CT 06510 USA

[8] Cleveland Clin, Dept Radiat Oncol, Taussig Canc Inst, Cleveland, OH 44106 USA

[9] Univ Alabama Birmingham, Birmingham, AL USA

[10] Vanderbilt Univ, Med Ctr, Dept Radiat Oncol, Nashville, TN USA

[11] Wake Forest Univ, Bowman Gray Sch Med, Winston Salem, NC USA

[12] Wake Forest Univ, Med Ctr, Winston Salem, NC USA

来源：

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | 2017年 / 99卷 / 02期

关键词：

D O I：

10.1016/j.ijrobp.2017.06.1614

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

3003

引用

页码：E422 / E423

页数：2

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